US8489596B1 - Apparatus and method for profiling users - Google Patents
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2457—Query processing with adaptation to user needs
- G06F16/24578—Query processing with adaptation to user needs using ranking
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0261—Targeted advertisements based on user location
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/029—Location-based management or tracking services
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
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- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0269—Targeted advertisements based on user profile or attribute
Definitions
- the present invention relates generally to user profiles and, more specifically, to generating user profiles based on user location.
- User profiles are useful in a variety of contexts. For example, advertisers often purchase advertising based on a desire to reach potential customers having particular attributes. Such advertisers often employ user profiles to select when, where, or how the advertiser conveys their message. Similarly, market researchers may analyze user profiles to better understand the market for a given good or service based on attributes of buyers of that good or service. In another example, user profiles may be used to customize products or services, for instance by customizing a software application according to the profile of a user of the software application.
- the present techniques include a process of profiling a user of a mobile computing device, the process including: obtaining a location history of a user, the location history being based on signals from a mobile computing device of the user; obtaining a location-attribute score of a location identified in, or inferred from, the location history; determining, with a computer, a user-attribute score based on the location-attribute score; and storing the user-attribute score in a user-profile datastore.
- Some aspects include a tangible, machine-readable, non-transitory medium storing instructions that when executed by a data processing apparatus, cause the data processing apparatus to perform operations including: obtaining a location history of a user, the location history being based on signals from a mobile computing device of the user; obtaining a location-attribute score of a location identified in, or inferred from, the location history; determining a user-attribute score based on the location-attribute score; and storing the user-attribute score in a user-profile datastore.
- Some aspects include a system, including: one or more processors; and memory storing instructions that when executed by the processors cause the processors to perform operations including: obtaining a location history of a user, the location history being based on signals from a mobile computing device of the user; obtaining a location-attribute score of a location identified in, or inferred from, the location history; determining a user-attribute score based on the location-attribute score; and storing the user-attribute score in a user-profile datastore.
- FIG. 1 shows an example of an environment in which a user profiler operates in accordance with some embodiments
- FIG. 2 shows an embodiment of a process for profiling a user
- FIG. 3 shows an embodiment of another process for profiling a user
- FIG. 4 shows an embodiment of a computer system by which the above-mentioned systems and processes may be implemented.
- FIG. 1 shows an example of a computing environment 10 having a user profiler 12 operative to generate user profiles to be stored in a user-profile datastore 14 .
- the user profiler 12 generates (for example, instantiates or updates) the user profiles based on location histories from mobile devices 16 and attributes of geographic locations stored in a geographic information system 18 .
- the resulting user profiles may reflect the attributes of the locations visited by users.
- the location histories may be conveyed via the Internet 20 to remote locations, and the user profiles may be used by advertisement servers 22 to select advertisements for presentation to users or for other purposes described below.
- the profiles may characterize a variety of attributes of users.
- a location history may indicate that a user frequently visits geographic locations associated with tourism, and the profile of that user may be updated to indicate that the user frequently engages in tourism, which may be of interest to certain categories of advertisers.
- a user may spend their working hours in geographic areas associated with childcare and residences, and based on their location history, the profile of that user may be updated to indicate that the user likely engages in childcare for children younger than school age.
- Other examples are described below.
- the attributes associated with geographic locations may vary over time (for example, an area with coffee shops and bars may have a stronger association with consumption of breakfast or coffee in the morning, an association which weakens in the evening, while an association with entertainment or nightlife may be weaker in the morning and stronger in the evening).
- User profiles may be generated in accordance with the time-based attributes that predominate when the user is in a geographic area. And in some embodiments, user profiles may also be segmented in time, such that a portion of a given user's profile associated with a weekday morning may have different attributes than another portion of that user's profile associated with a weekend night, for instance.
- the user profiles may be used by advertisers and others in a privacy-friendly fashion, such that users are expected to tend to opt in to sharing their location history.
- the user profiles may be aggregated to identify geographic areas having a high density of a particular type of user at a particular time of the week, such as a sports stadium having a relatively large number of users associated with fishing as a hobby, or a children's soccer field in which a relatively large number of people associated with golfing as a hobby might tend to co-occur on weekend mornings.
- Such correlations may be presented to advertisers or others without disclosing information by which individual users can be uniquely identified.
- user-specific information may be provided, for example, users who opt in to sharing their profiles may receive user-specific services or communications formulated based on the individual profile of that user.
- the user profiler 12 obtains data from the mobile devices 16 and the geographic information system 18 to output user profiles to the user-profile datastore 14 for use by the ad servers 22 or for other purposes. Accordingly, these components are described in this sequence, starting with inputs, and concluding with outputs.
- the mobile devices 16 maybe any of a variety of different types of computing devices having an energy storage device (e.g., a battery) and being capable of communicating via a network, for example via a wireless area network or a cellular network connected to the Internet 20 .
- the mobile devices 16 are handheld mobile computing devices, such as smart phones, tablets, or the like, or the mobile devices may be laptop computers or other special-purpose computing devices, such as an automobile-based computer (e.g., an in-dash navigation system).
- the mobile devices 16 may have a processor and a tangible, non-transitory machine-readable memory storing instructions that provide the functionality described herein when executed by the processor.
- the memory may store instructions for an operating system, special-purpose applications (apps), and a web browser, depending upon the use case.
- apps special-purpose applications
- web browser a web browser
- the present techniques are not limited to mobile devices, and other computing devices subject to geolocation may also generate data useful for forming user profiles. For instance, set-top boxes, gaming consoles, or Internet-capable televisions may be geolocated based on IP address, and data from user interactions with these devices may be used to update user profiles, e.g., with user interaction indicating a time at which a user was at the geolocation corresponding to the device.
- This software may have access to external or internal services by which the location of the mobile device may be obtained.
- the mobile device may have a built-in satellite-based geolocation device (for instance a global-positioning system, or GPS, device or components operative to obtain location from other satellite-based systems, such as Russia's GLONASS system or the European Union's Galileo system).
- location may be obtained based on the current wireless environment of the mobile device, for example by sensing attributes of the wireless environment (e.g. SSIDs of wireless hotspots, identifiers of cellular towers, and signal strengths) and sending those attributes to a remote server capable of identifying the location of the mobile device.
- attributes of the wireless environment e.g. SSIDs of wireless hotspots, identifiers of cellular towers, and signal strengths
- the location may be obtained based on an identifier of a network node through which the mobile device connects to the Internet, for example by geocoding an IP address of a wireless router or based on a location of a cellular tower to which the mobile device is connected.
- the location may be expressed as a latitude and longitude coordinate or an area, and in some cases may include a confidence score, such as a radius or bounding box defining area within which the device is expected to be with more than some threshold confidence.
- the location of the mobile devices 16 may be obtained by the mobile devices.
- the application may have permission to obtain the location of the mobile device and report that location to a third party server associated with the application, such that the location may be obtained by the user profiler 12 from the third party server.
- the user may visit a website having code that obtains the current location of the mobile device. This location may be reported back to the server from which the website was obtained or some other third party server, such as an ad server for an affiliate network, and location histories may be obtained from this server.
- locations of the mobile devices 16 may be obtained without the participation of the mobile device beyond connecting to a network.
- users may opt in to allowing a cellular service provider to detect their location based on cellular signals and provide that location to the user profiler 12 .
- the location may be acquired intermittently, for example at three different times during a day when a user launches a particular application, or relatively frequently, for example by periodically polling a GPS device and reporting the location.
- the location history may include locations obtained more than one-second apart, more than one-minute apart, more than one-hour apart, or more, depending upon the use case.
- Locations may be obtained in real time from mobile devices 16 by the user profiler 12 , or in some embodiments, location histories may be obtained.
- Each location history may include records of geographic locations of a given mobile device and when the mobile device was at each location.
- a location history may include records of location over a relatively long duration of time, such as more than over a preceding hour, day, week, or month, as some modes of acquiring location histories report or update location histories relatively infrequently.
- a location history for a given mobile device may include a plurality (e.g., more than 10 or more than 100) location records, each location record corresponding to a detected location of the mobile device, and each location record including a geographic location and the time at which the mobile device was at the location.
- the location records may also include a confidence score indicative of the accuracy of the detected location.
- Geographic locations may be expressed in a variety of formats with varying degrees of specificity, for example as a latitude and longitude coordinates, as tiles in a grid with which a geographic area is segmented (e.g., quantized), or in some other format for uniquely specifying places.
- the geographic information system 18 may be configured to provide information about geographic locations in response to queries specifying a location of interest.
- the geographic information system 18 organizes information about a geographic area by quantizing (or otherwise dividing) the geographic area into area units, called tiles, that are mapped to subsets of the geographic area.
- the tiles correspond to square units of area having sides that are between 10-meters and 1000-meters, for example approximately 100-meters per side, depending upon the desired granularity with which a geographic area is to be described.
- the attributes of a geographic area change over time. Accordingly, some embodiments divide each tile according to time. For instance, some embodiments divide each tile into subsets of some period of time, such as one week, one month, or one year, and attributes of the tile are recorded for subsets of that period of time. For example, the period of time may be one week, and each tile may be divided by portions of the week selected in view of the way users generally organize their week, accounting, for instance, for differences between work days and weekends, work hours, after work hours, mealtimes, typical sleep hours, and the like.
- Examples of such time divisions may include a duration for a tile corresponding to Monday morning from 6 AM to 8 AM, during which users often eat breakfast and commute to work, 8 AM till 11 AM, during which users often are at work, 11 AM till 1 PM, during which users are often eating lunch, 1 PM till 5 PM, during which users are often engaged in work, 5 PM till 6 PM, during which users are often commuting home, and the like. Similar durations may be selected for weekend days, for example 8 PM till midnight on Saturdays, during which users are often engaged in leisure activities. Each of these durations may be profiled at each tile.
- the geographic information system 18 includes a plurality of tile records, each tile record corresponding to a different subset of a geographic area.
- Each tile record may include an identifier, an indication of geographic area corresponding to the tile (which for regularly size tiles may be the identifier), and a plurality of tile-time records.
- Each tile-time record may correspond to one of the above-mentioned divisions of time for a given tile, and the tile-time records may characterize attributes of the tile at different points of time, such as during different times of the week.
- Each tile-time record may also include a density score indicative of the number of people in the tile at a given time.
- each tile-time record includes an indication of the duration of time described by the record (e.g. lunch time on Sundays, or dinnertime on Wednesdays) and a plurality of attribute records, each attribute record describing an attribute of the tile at the corresponding window of time during some cycle (e.g., weekly).
- each tile-time record may include a relatively large number of attribute records, for example more than 10, more than 100, more than 1000, or approximately 4000 attribute records, depending upon the desired specificity with which the tiles are to be described.
- Each attribute record may include an indicator of the attribute being characterized and an attribute score indicating the degree to which users tend to engage in activities corresponding to the attribute in the corresponding tile at the corresponding duration of time.
- the attribute score (or tile-time record) is characterized by a density score indicating the number of users expected to engage in the corresponding activity in the tile at the time.
- a query may be submitted to determine what sort of activities users engage in at a particular block in downtown New York during Friday evenings, and the geographic information system 18 may respond with the attribute records corresponding to that block at that time.
- Those attribute records may indicate a relatively high attribute score for high-end dining, indicating that users typically go to restaurants in this category at that time in this place, and a relatively low attribute score for playing golf, for example.
- Attribute scores may be normalized, for example a value from 0 to 10, with a value indicating the propensity of users to exhibit behavior described by that attribute.
- the user profiler 12 may join the location histories and tile records implicated by locations in those location histories to generate user profiles. Thus, users may be characterized according to the attributes of the places those users visit at the time the user visits those places.
- the generated user profiles may then be stored by the user profiler 12 in the user-profile datastore 14 , as described below.
- some embodiments of the user profiler 12 includes a location-history acquisition module 24 , a location-attribute acquisition module 26 , and a user-attribute updater 28 operative to generate user profiles.
- the user profiler 12 may be constructed from one or more of the computers described below with reference to FIG. 4 . These computers may include a tangible, non-transitory, machine-readable medium, such as various forms of memory storing instructions that when executed by one or more processors of these computers (or some other data processing apparatus) cause the computers to provide the functionality of the user profiler 12 described herein.
- the components of the user profiler 12 are illustrated as discrete functional blocks, but it should be noted that the hardware and software by which these functional blocks are implemented may be differently organized, for example, code or hardware for providing the this functionality may be intermingled, subdivided, conjoined, or otherwise differently arranged.
- the illustrated location-history acquisition module 24 may be configured to acquire location histories of mobile devices 16 via the Internet 20 .
- the location histories may be acquired directly from the mobile devices 16 , or the location histories may be acquired from various third parties, such as a third-party hosting Web applications rendered on the mobile devices 16 , third parties hosting servers to which location histories are communicated by apps on the mobile devices 16 , or third parties providing network access to the mobile devices 16 , such as cellular service providers, for example.
- the location-history acquisition module 24 may include a plurality of sub-modules for obtaining location histories from a plurality of different providers. These sub-modules may be configured to request, download, and parse location histories from a respective one of the different providers via application program interfaces provided by those providers. The sub-modules may translate the location histories from the different providers, which may be in different formats, into a common format for use in subsequent processing. Location histories may be acquired periodically, for example monthly, weekly, or hourly, or more frequently.
- the user profiler 12 of this embodiment further includes the location-attribute acquisition module 26 .
- the module 26 may be configured to obtain attributes of locations identified based on the location histories acquired by the location history acquisition module 24 .
- the module 26 may be configured to iterate through each location identified by each location history and query the geographic information system 18 for attributes of those locations at the time at which the user was at the corresponding location.
- the location-attribute acquisition module 26 may also request attributes of adjacent locations, such as adjacent tiles, from the geographic information system 18 so that the user-attribute updater 28 can determine whether a signal from a given tile is consistent with that of surrounding tiles for assessing the reliability of various indications.
- the acquired location histories and location attributes may be provided by modules 24 and 26 to the user-attribute updater 28 , which in some embodiments, is configured to generate user profiles based on this data.
- the user-attribute updater 28 is operative to perform portions of the processes of FIG. 2 or 3 , described in detail below, and attach attributes of places visited by users to the profile of those users. These profiles may be stored by the user attribute updater 28 in the user-profile datastore 14 .
- the user profile datastore 14 may be operative to store user profiles and, in some embodiments, address queries for data in the user profiles.
- the illustrated user-profile datastore 14 includes a plurality of user-profile records, each record corresponding to the profile of a given user or a given mobile device 16 .
- Each user-profile record may include an identifier of the record (which may be a value otherwise uncorrelated with the identity of the user to enhance privacy), and an identifier of the source or sources of the location histories from which the profile was created such that subsequent location histories can be matched with the profile (e.g. a account associated with a special-purpose application, a cell phone number, or some other value, which may be hashed to enhance user privacy).
- Each user-profile record may also include a plurality of profile time records indicating attributes of the user profile at different times during some cycle of time (e.g., portions of the week or month, or during other periods like those described above with reference to the geographic information system 18 ).
- the profile-time records may correspond to the same durations of time as those of the time-tile records described above.
- Each profile-time record may include an indication of the duration of time being described (e.g. Thursday's at dinnertime, or Saturday midmorning) and a plurality of profile attribute records, each profile attribute record indicating the propensity of the corresponding user to engage in an activity described by the attribute during the corresponding time of the profile-time record.
- the profile time records may allow tracking of when users tend to engage in a given activity (time of day, day of week, week of year).
- the profile attribute records correspond to the same set of attribute records described above with reference to the geographic information system 18 .
- Each profile-attribute record may include an indication of the attribute being characterized (e.g., attending a children's soccer game, having brunch at a fast-casual dining establishment, parent running errands, or shopping at a mall) and a score indicating the propensity of the user to engage in the activity at the corresponding time, such as a normalized value from 0 to 10.
- the attribute records may further include a sample size, indicative of the number of samples upon which the attribute score is based, for weighting new samples, and a measure of variance among these samples (e.g., a standard deviation) for identifying outliers.
- the user-profile records may be used for a variety of purposes. For example, advertisers operating ad servers 22 may submit to the user-profile datastore 14 a query identifying one of the user-profile records, such as the above-mentioned hashed value of a user account number or phone number, and the user-profile datastore 14 may respond with the attributes of the corresponding user at the current time.
- queries may be submitted for a specific attribute to determine whether to serve an advertisement corresponding to the attribute, or a query may request a binary indication of whether the attribute score is above a threshold.
- the user-profile datastore 14 may be used by the user profiler 12 to augment the records in the geographic information system 18 .
- an index may be created for each attribute that identifies tiles where users having relatively strong scores (e.g. above a threshold) for the respective attribute tend to co-occur at given times. These indices may correspond to heat maps (though no visual representation need be created) indicating where, for example, users interested in golf, tend to be during various times of the day, such that advertisers can select advertisements based on this information.
- an index may be created for each user attribute at each of the above-described divisions of time in the geographic information system 18 , and these indices may be queried to provide relatively prompt responses relating to where users having a given attribute or combination of attributes tend to co-occur at various times. Precalculating the indices is expected to yield faster responses to such queries than generating responsive data at the time the query is received. For instance, using examples of these indices relating to fishing and employment in banking, an advertiser may determine that people who engage in fishing on the weekend and work in banking tend to drive relatively frequently along a particular stretch of road on Mondays during the evening commute, and that advertiser may purchase an advertisement for bass fishing boats on a billboard along that road in response. Other examples relating to customization of software and services and other forms of analysis are described in greater detail below.
- some embodiments of the computing environment 10 generate user profiles that are relatively privacy-friendly to users and consume relatively little effort on the part of users or others to create the profiles. These advantages are expected to yield a relatively comprehensive set of relatively high-resolution user profiles that may be used by advertisers and others seeking to provide information and services customized to the unique attributes of each user, facilitating the presentation of high-value and high-relevance advertisements and services to users while respecting the users' interest in privacy. That said, not all embodiments provide these benefits, and some embodiments may forgo some or all of these embodiments in the interest of various engineering trade-offs relating to time, cost, and features.
- FIG. 2 illustrates an embodiment of a process 30 that may be performed by the above-describes user profiler 12 .
- the steps of the process 30 may be performed in a different order than the order in which the steps are described.
- the process 30 includes obtaining a location history of a user, as illustrated by block 32 . This step may be performed by the above-described location-history acquisition module 24 .
- location histories may be obtained from a plurality of different providers of location histories, and the location histories may be reformatted into a common format for subsequent processing.
- the process 30 of this embodiment further includes obtaining a location-attribute score of a location identified in, or inferred from, the location history, as indicated by block 34 .
- This step may be performed by the above-described location-attribute acquisition module 26 .
- the location-attribute score may be one of a plurality of scores corresponding to a time-tile record described above.
- locations identified in the location history may be relatively sparse, and intermediate locations between those explicitly identified may be inferred.
- the user profiler 12 may determine that two locations are more than a threshold amount of time apart and a threshold distance apart, indicating that the user likely traveled between the location during the intermediate time.
- the user profiler 12 may query the geographic information system 18 for locations associated with travel, such as locations corresponding to an interstate highway, between the two locations, and the locations along the interstate highway (or associated with some other mode of travel) may be added to the location history at the intermediate times as inferred locations. Inferring intermediate locations is expected to yield a more comprehensive characterization of the user's profile.
- the process 30 further includes storing the user-attribute score in a user-profile datastore, as indicated by block 38 .
- this may include updating indices corresponding to various attributes in a geographic information system, and the stored user profiles may be queried by advertisers and others seeking to provide targeted messaging and services. Targeting may be toward specific users who are profiled or to the places profiled users visit or based on patterns in attribute scores among profiled users.
- the process 40 in this embodiment proceeds to identify a time-bin corresponding to a time-stamp of the next location in the location history, as indicated by block 48 .
- the time-bin may be one of the above-describes durations of time by which tiles or user profiles are characterized.
- the corresponding time bin may be the time bin in which the timestamp of the use location falls.
- this embodiment of process 40 includes querying a geographic information system for tile records of the tiles corresponding to the location and adjacent tiles, as indicated by block 50 .
- the process 40 further includes determining whether the user is likely at an adjacent location, as indicated by block 52 .
- determining whether the user is likely at an adjacent location may include making the determination based on the attributes of the adjacent tiles or density scores for the tiles corresponding to the timestamp of the user location in question.
- attribute scores for the location in the location history may indicate that less than a threshold amount of user activity occurs within the tile corresponding to that location (e.g., a density value indicative of the number of people in the tile may be below a threshold)
- attribute scores for one of the adjacent tiles may indicate a relatively high density or amount of activity (e.g., more than a threshold, or more than a threshold difference from the adjacent tile) for one or more attributes.
- the adjacent location may be selected as being a more likely location of the user.
- Some embodiments may select the adjacent location having the highest density or aggregation of attribute scores, for example.
- some embodiments may designate the adjacent location as the user location, as indicated by block 54 , or in response to a negative determination in block 52 , some embodiments may proceed to the next step without such a designation occurring.
- Other embodiments may calculate a confidence score based on the similarity of adjacent tiles and weight the modification of the user profile based on the confidence score, down weighting signals in instances in which the adjacent tiles are relatively different from one another, or a binary determination may be made as illustrated in FIG. 3 .
- some embodiments return to block 46 . Alternatively, the process may proceed to the next step.
- the determination of step 58 is made for each of a plurality of attributes of the location, and those attributes deemed to be outliers may be filtered before proceeding to the next step, or some embodiments may return to step 46 in response to the detection of an outlier.
- Some implementations may use a similarity model to detect inaccurate signals in acquired location histories. Using such models, embodiments may filter out questionable location readings so as not to pollute profile development. For instance, a reliability database similar to the GIS may be referenced during profile analysis by submitting a query with metadata about entries in a location history (e.g., publisher, time of day, location, OS, device type, location determination method (e.g. GPS vs. WiFiTM or other wireless network)).
- a location history e.g., publisher, time of day, location, OS, device type, location determination method (e.g. GPS vs. WiFiTM or other wireless network)
- various indices may be formed to expedite certain queries to the geographic information system 18 .
- some embodiments may form an index keyed to each attribute for which a score is maintained in the tile records or the user-profile records.
- embodiments may calculate an index that identifies each tile in which users having more than a threshold score for a given attribute co-occur during one of the above-described time bins (e.g., by multiplying a density score for each tile with an attribute score and thresholding the resulting product). This index may be used to relatively quickly determine whether a given geographic area at a given time is correlated with a given attribute and has a high density of people exhibiting behavior described by that attribute.
- some embodiments may use such an index to identify geographic areas in which a collection of attributes are relatively strong, for instance determining the union of the set of values corresponding to each of a plurality of different attributes to identify, for instance, where users associated with golfing, fishing, and tourism are at a relatively high concentration on mid-afternoons on Sundays.
- the above technique may be used to analyze each of a plurality of tiles concurrently with different computing devices mapped to different tiles, and then the technique of this paragraph may be used to aggregate this data for each user profile/attribute pair across the tiles, e.g., by querying the devices analyzing tiles for data about a given user and attribute and summing (or otherwise aggregating) the responses.
- this technique is expected to offer relatively fast concurrent operation and reduce calls to data stores that might otherwise slow the analysis.
- FIG. 4 is a diagram that illustrates an exemplary computing system 1000 in accordance with embodiments of the present technique.
- Various portions of systems and methods described herein may include or be executed on one or more computer systems similar to computing system 1000 . Further, processes and modules described herein may be executed by one or more processing systems similar to that of computing system 1000 .
- Processes, such as logic flows, described herein may be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating corresponding output. Processes described herein may be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).
- Computing system 1000 may include a plurality of computing devices (e.g., distributed computer systems) to implement various processing functions.
- I/O device interface 1030 may provide an interface for connection of one or more I/O devices 1060 to computer system 1000 .
- I/O devices may include devices that receive input (e.g., from a user) or output information (e.g., to a user).
- I/O devices 1060 may include, for example, graphical user interface presented on displays (e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor), pointing devices (e.g., a computer mouse or trackball), keyboards, keypads, touchpads, scanning devices, voice recognition devices, gesture recognition devices, printers, audio speakers, microphones, cameras, or the like.
- I/O devices 1060 may be connected to computer system 1000 through a wired or wireless connection.
- I/O devices 1060 may be connected to computer system 1000 from a remote location.
- I/O devices 1060 located on remote computer system for example, may be connected to computer system 1000 via a network and network interface 1040 .
- Network interface 1040 may include a network adapter that provides for connection of computer system 1000 to a network.
- Network interface may 1040 may facilitate data exchange between computer system 1000 and other devices connected to the network.
- Network interface 1040 may support wired or wireless communication.
- the network may include an electronic communication network, such as the Internet, a local area network (LAN), a wide area (WAN), a cellular communications network or the like.
- System memory 1020 may be configured to store program instructions 1100 or data 1110 .
- Program instructions 1100 may be executable by a processor (e.g., one or more of processors 1010 a - 1010 n ) to implement one or more embodiments of the present techniques.
- Instructions 1100 may include modules of computer program instructions for implementing one or more techniques described herein with regard to various processing modules.
- Program instructions may include a computer program (which in certain forms is known as a program, software, software application, script, or code).
- a computer program may be written in a programming language, including compiled or interpreted languages, or declarative or procedural languages.
- a computer program may include a unit suitable for use in a computing environment, including as a stand-alone program, a module, a component, a subroutine.
- a computer program may or may not correspond to a file in a file system.
- a program may be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code).
- a computer program may be deployed to be executed on one or more computer processors located locally at one site or distributed across multiple remote sites and interconnected by a communication network.
- System memory 1020 may include a tangible program carrier having program instructions stored thereon.
- a tangible program carrier may include a non-transitory computer readable storage medium.
- a non-transitory computer readable storage medium may include a machine readable storage device, a machine readable storage substrate, a memory device, or any combination thereof.
- Non-transitory computer readable storage medium may include, non-volatile memory (e.g., flash memory, ROM, PROM, EPROM, EEPROM memory), volatile memory (e.g., random access memory (RAM), static random access memory (SRAM), synchronous dynamic RAM (SDRAM)), bulk storage memory (e.g., CD-ROM and/or DVD-ROM, hard-drives), or the like.
- non-volatile memory e.g., flash memory, ROM, PROM, EPROM, EEPROM memory
- volatile memory e.g., random access memory (RAM), static random access memory (SRAM), synchronous dynamic RAM (SDRAM)
- bulk storage memory e.g
- System memory 1020 may include a non-transitory computer readable storage medium may have program instructions stored thereon that are executable by a computer processor (e.g., one or more of processors 1010 a - 1010 n ) to cause the subject matter and the functional operations described herein.
- a memory e.g., system memory 1020
- the program may be conveyed by a propagated signal, such as a carrier wave or digital signal conveying a stream of packets.
- I/O interface 1050 may be configured to coordinate I/O traffic between processors 1010 a - 1010 n , system memory 1020 , network interface 1040 , I/O devices 1060 and/or other peripheral devices. I/O interface 1050 may perform protocol, timing or other data transformations to convert data signals from one component (e.g., system memory 1020 ) into a format suitable for use by another component (e.g., processors 1010 a - 1010 n ). I/O interface 1050 may include support for devices attached through various types of peripheral buses, such as a variant of the Peripheral Component Interconnect (PCI) bus standard or the Universal Serial Bus (USB) standard.
- PCI Peripheral Component Interconnect
- USB Universal Serial Bus
- Embodiments of the techniques described herein may be implemented using a single instance of computer system 1000 , or multiple computer systems 1000 configured to host different portions or instances of embodiments. Multiple computer systems 1000 may provide for parallel or sequential processing/execution of one or more portions of the techniques described herein.
- Computer system 1000 is merely illustrative and is not intended to limit the scope of the techniques described herein.
- Computer system 1000 may include any combination of devices or software that may perform or otherwise provide for the performance of the techniques described herein.
- computer system 1000 may include or be a combination of a cloud-computing system, a data center, a server rack, a server, a virtual server, a desktop computer, a laptop computer, a tablet computer, a server device, a client device, a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a vehicle-mounted computer, or a Global Positioning System (GPS), or the like.
- PDA personal digital assistant
- GPS Global Positioning System
- Computer system 1000 may also be connected to other devices that are not illustrated, or may operate as a stand-alone system.
- the functionality provided by the illustrated components may in some embodiments be combined in fewer components or distributed in additional components.
- the functionality of some of the illustrated components may not be provided or other additional functionality may be available.
- instructions stored on a computer-accessible medium separate from computer system 1000 may be transmitted to computer system 1000 via transmission media or signals such as electrical, electromagnetic, or digital signals, conveyed via a communication medium such as a network or a wireless link.
- Various embodiments may further include receiving, sending or storing instructions or data implemented in accordance with the foregoing description upon a computer-accessible medium. Accordingly, the present invention may be practiced with other computer system configurations.
- the word “may” is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must).
- the words “include”, “including”, and “includes” and the like mean including, but not limited to.
- the singular forms “a”, “an” and “the” include plural referents unless the content explicitly indicates otherwise.
- a special purpose computer or a similar special purpose electronic processing or computing device is capable of manipulating or transforming signals, for instance signals represented as physical electronic, optical, or magnetic quantities within memories, registers, or other information storage devices, transmission devices, or display devices of the special purpose computer or similar special purpose processing or computing device.
Abstract
Description
Claims (21)
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Also Published As
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JP2016505990A (en) | 2016-02-25 |
JP6275745B2 (en) | 2018-02-07 |
US20170017661A1 (en) | 2017-01-19 |
EP2941750A1 (en) | 2015-11-11 |
EP2941750A4 (en) | 2016-08-24 |
WO2014107569A1 (en) | 2014-07-10 |
US20140195530A1 (en) | 2014-07-10 |
US20160147790A1 (en) | 2016-05-26 |
US9275114B2 (en) | 2016-03-01 |
US9483498B2 (en) | 2016-11-01 |
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